A Joint Model of Random Forest and Artificial Neural Network for the Diagnosis of Endometriosis

Endometriosis (EM), an estrogen-dependent inflammatory disease with unknown etiology, affects thousands of childbearing-age couples, and its early diagnosis is still very difficult. With the rapid development of sequencing technology in recent years, the accumulation of many sequencing data makes it possible to screen important diagnostic biomarkers from some EM-related genes. In this study, we utilized public datasets in the Gene Expression Omnibus (GEO) and Array-Express database and identified seven important differentially expressed genes (DEGs) (COMT, NAA16, CCDC22, EIF3E, AHI1, DMXL2, and CISD3) through the random forest classifier. Among these DEGs, AHI1, DMXL2, and CISD3 have never been reported to be associated with the pathogenesis of EMs. Our study indicated that these three genes might participate in the pathogenesis of EMs through oxidative stress, epithelial–mesenchymal transition (EMT) with the activation of the Notch signaling pathway, and mitochondrial homeostasis, respectively. Then, we put these seven DEGs into an artificial neural network to construct a novel diagnostic model for EMs and verified its diagnostic efficacy in two public datasets. Furthermore, these seven DEGs were included in 15 hub genes identified from the constructed protein–protein interaction (PPI) network, which confirmed the reliability of the diagnostic model. We hope the diagnostic model can provide novel sights into the understanding of the pathogenesis of EMs and contribute to the clinical diagnosis and treatment of EMs.

INTRODUCTION
Endometriosis (EM) is an estrogen-dependent inflammatory disorder, which afflicts about 10%-15% of women of childbearing age (Parasar et al., 2017). It is defined as the presence of endometrial-like tissue outside of the uterine cavity, which can lead to chronic pelvic pain, and infertility (Drabble et al., 2021). However, the true prevalence of EMs is uncertain as visual laparoscopy is the gold standard for the diagnosis of EMs (Taylor et al., 2018). At the moment, Sampson's theory of menstrual blood reflux observed in most patients is commonly accepted in the pathophysiology of EMs, while only a small portion will develop into this disease (Burney and Giudice, 2012). However, it could only explain a portion of EMs. Therefore, it's necessary to further investigate a comprehensive understanding of the pathogenesis of EMs and find effective molecular biomarkers to improve the early diagnosis and treatment of EMs.
DNA microarray technology is a high-throughput detection method that can be used to provide gene expression profiles and thus can help to screen disease-related genes and biomarkers (Yoo et al., 2009). With the rapid development of DNA microarray technology, a large amount of high-throughput data has accumulated available on public platforms. However, how to make effective use of these data to screen critical disease-related genes for the diagnosis of EMs is a great challenge. At present, random forest and neural network are widely applied for disease prediction (Yigit and Isik, 2018;Khan et al., 2019;Shaia et al., 2019;Kugunavar and Prabhakar, 2021). Among them, random forest algorithm can perform random sampling to screen the target variables (Schonlau and Zou, 2020) and has high predicted accuracy (Byeon, 2019;Chen et al., 2020). Furthermore, the artificial neural network can be used to evaluate the accuracy of predicted model with divided training and validation datasets (Curchoe et al., 2020). Currently, there are some useful visualization and analysis tools for neural networks, such as NeuralNetTools (Beck, 2018), spiking neuronal networks (Galindo et al., 2020), and Net2Vis (Bauerle et al., 2021).
Therefore, the combination of random forest and artificial neural network would have better classification performance and more meaningful selected features (Kong and Yu, 2018;Tian et al., 2020). In this study, we firstly identified some differentially expressed genes (DEGs) between EMs and normal samples from public datasets in the Gene Expression Omnibus (GEO) database. Through the random forest classifier, we screened these DEGs and obtained seven important DEGs (COMT, NAA16, CCDC22, EIF3E, AHI1, DMXL2, and CISD3). Then, we put these seven DEGs into an artificial neural network to construct a novel diagnostic model and verified its diagnostic efficacy in two public datasets (See the detailed process in Figure 1). We hope this diagnostic model can provide novel sights into the pathogenesis of EMs and improve the early diagnosis and treatment of EMs.

Data Download and Processing
The GSE51981, GSE6364, and GSE7307 datasets were downloaded by the R package "GEOquery" (2.60.0) (Davis and Meltzer, 2007) to obtain the expression profile data. Then, the E-MTAB-694 dataset was downloaded through the Array-Express database. The related annotation information including the platforms, the probes, and ID conversion was obtained from the GEO database. When multiple probes corresponded to one gene symbol, the average expression level of multiple probes was used as the expression level of the corresponding gene. ID conversion was conducted with the R package "org.Hs.eg.db" (v3.13.0). Furthermore, the "removeBatchEffect" function in the R package "LIMMA" (v3.48.3) (Ritchie et al., 2015) was used to adjust batch effects, which were evaluated by principal component analysis (PCA).

Differential Expression and Functional Enrichment Analysis
Differential expression analysis was conducted on 77 EM disease and 71 normal samples of the GSE51981 dataset through the Bayesian analysis of the R package "LIMMA". The log2FoldChange > 1.5 and p-value < 0.05 were set as the threshold of DEGs. The R package "pheatmap" (v1.0.12) was used to perform clustering analysis of DEGs for the heatmap. To explore the biological significance of these DEGs in the pathogenesis of EMs, GO and KEGG pathway enrichment analyses were performed through the R package "clusterProfiler" (v4.1.3) (Wu et al., 2021) to identify significantly enriched GO terms and significantly enriched KEGG pathways with the threshold of p-value < 0.05.

The Construction of Hub Gene Network
The STRING (v11.5) (https://string-db.org/cgi/input.pl) (Szklarczyk et al., 2021) has been widely applied to construct a protein-protein interaction (PPI) network. Based on those DEGs, the "Multiple proteins" option was selected. In the PPI network, the minimum required interaction score was set as "high confidence (0.700)". Then, the cytoHubba (Chin et al., 2014) was employed to identify hub genes. The eccentricity algorithm was selected and 15 top-ranked genes were chosen as hub genes. Finally, the hub gene network was visualized with Cytoscape (v3. 9.0) (Demchak et al., 2014).

Screening Differentially Expressed Genes With the Random Forest Model
The R package "randomForest" (v4.6.14) (Liaw et al., 2014) was used to construct a random forest model to screen DEGs. The number of random seeds and decision trees was set as 1-5,000 and 3,000 in the random forest classifier originally, respectively. Finally, the number of random seeds and decision trees was set as 4,543 and 219, respectively, which represented higher accuracy of the constructed model and stable model error. The Gini coefficient method was used to obtain the dimensional importance value of all variables from the constructed random forest model. Those DEGs with an importance value greater than 4 were screened as important genes of EMs for subsequent model construction and verification. The R package "pheatmap" was used to perform clustering analysis of the screened important genes for the heatmap in this dataset.

The Construction and Verification of the Artificial Neural Network Model
The GSE6364 dataset downloaded through the R package "GEOquery" was selected as the training set for the construction of the artificial neural network model. After the data normalization, the R package "neuralnet" (v1.44.2) (Fritsch and Guenther, 2016) was used to construct an artificial neural network model of those important variables. The number of hidden neuron layers should be two-thirds of the number of the input layer plus two-thirds of the number of the output layer. Therefore, six hidden layers were set as the model parameter to construct a classification model of EMs through the predicted gene weight information. The R packages "pROC" (v1.18.0) (Robin et al., 2011) and "ggplot2" (v3.3.5) (Gómez-Rubio, 2017) were used to calculate the verification results of AUC classification performance and draw the ROC curve. Another two datasets E-MTAB-694 and GSE7307 were used to verify the accuracy of the constructed neural network model for the diagnosis of EMs. The R package "pROC" was used to draw ROC curves for each dataset, and the AUC value was calculated to verify the classification efficiency. Meanwhile, the sensitivity and specificity in distinguishing the disease samples from normal samples were calculated.

Data Processing and Differential Expression Analysis
The R package "GEOquery" was used to download the GEO dataset GSE51981 (77 EM disease samples and 71 normal samples) and obtain detailed information. We used the "removeBatchEffect" function in the R package "LIMMA" to adjust batch effects and then conducted principal component analysis (PCA) analysis to evaluate the performance of batch effect adjustment. PCA results (Supplementary Figure S1) indicated that the disease samples were mixed with the normal samples, which suggested the challenge of diagnosing. We also used the R package "LIMMA" to perform differential expression analysis for the dataset GSE51981 through the Bayesian test. We finally identified 2,267 significantly upregulated and 285 significantly downregulated expressed genes between the disease samples and the normal samples with the threshold of fold change values of >1.5 and p < 0.05. The detailed information of all DEGs is listed in Supplementary Table S1. The results of these DEGs and the heatmap of these DEGs are visualized in Figures 2A and 2B, respectively.

Functional Enrichment Analysis for DEGs and the Construction of PPI Network
To explore the biological significance of these DEGs in the pathogenesis of EMs, we performed GO and KEGG pathway enrichment analyses through the R package 'clusterProfiler'. GO terms were classified into three categories: biological process (BP), cellular component (CC), and molecular function (MF). The top five GO terms of genes with significantly upregulated and downregulated expression levels were visualized in Figures 3A,B.
The GO enrichment analysis results indicated that these significantly upregulated expressed genes were mainly involved in the transmembrane transporter activity, ATPase activity, metallopeptidase activity, aldehyde dehydrogenase NADP + activity, and lipid transporter activity ( Supplementary  Table S2), while these significantly downregulated expressed genes were mainly involved in the flavin adenine dinucleotide binding, acyl-CoA dehydrogenase activity, phosphatidylcholine transporter activity, extracellular matrix structural constituent, and ATPase-coupled intramembrane lipid transporter activity (Supplementary Table S3). For KEGG pathway enrichment analysis, the results indicated that these upregulated expressed genes were significantly associated with the cAMP signaling pathway, adrenergic signaling in cardiomyocytes, aldosterone synthesis and secretion, ABC transporters, and salivary secretion (Supplementary Table S4), while these downregulated expressed genes were significantly associated with fatty acid degradation and metabolism; valine, leucine, and isoleucine degradation; lysosome; the PPAR signaling pathway; and the Hippo signaling pathway ( Supplementary  Table S5). Furthermore, we constructed a PPI network through the STRING database. The hub genes selected from the PPI network are shown in Supplementary Figure S2.
According to the eccentricity scores, we identified 15 hub genes from the network, which had highest confidence scores.

Constructing the Random Forest Model to Screen Differentially Expressed Genes
To screen DEGs, we put these DEGs into the random forest classifier and set the number of random seeds to 4,543. By referring to the relationship between the model error and the number of decision trees ( Figure 4A), we selected 219 trees as the parameter of the random forest model, which represented a stable error in the model. In the modeling process, we used the Gini coefficient method to measure the importance of all variables according to decreased mean square error and model accuracy ( Figure 4B). Finally, we selected seven DEGs (AHI1, DMXL2, NAA16, CCDC22, CISD3, COMT, and EIF3E) with a mean decrease of Gini index greater than 4 as important variables for subsequent analysis. Interestingly, all these DEGs were included in the 15 hub genes identified from the constructed PPI network. Among these variables, AHI1 was the most important, with the mean decrease of the Gini index being much higher than other variables (Supplementary Table S6). A small number of variables meant a small out-of-band error, which represented a high accuracy of the constructed random forest model. Based on these seven variables, we performed the k-means clustering of the dataset. The results suggested that these seven genes could be used to distinguish the disease sample from the normal samples ( Figure 4C). Furthermore, AHI1, DMXL2, and NAA16 genes were clustered as a group with low expression in the normal sample and high expression in the disease sample. On the contrary, CCDC22, CISD3, COMT, and EIF3E were clustered as another group with high expression in the normal sample and low expression in the disease sample.

The Construction of the Artificial Neural Network Model and the Evaluation of the ROC Curve
Based on the R package 'neuralnet', we use the GSE6364 dataset (21 disease samples and 21 normal samples) as the training set to construct the artificial neural network model. Firstly, we performed the preprocessing and normalization of this dataset. According to the output results of the neural network model ( Figure 5A), it is illuminated that the entire training was performed in 11,684 steps. Among the output results, the predicted weights of each hidden neuron layer were −3.97906, 1.04457, 2.76611, −2.00181, −11.84206, and −0.90829 (Supplementary Table S7). Next, we drew the ROC curve to evaluate the predicted performance; the AUC values of AHI1, COMT, DMXL2, CISD3, NAA16, EIF3E, and CCDC22 were 0.7150, 0.7809, 0.6927, 0.7266, 0.7217, 0.7093, and 0.7050, respectively ( Figure 5B). The larger the AUC value of each DEG is, the higher the credibility of the constructed diagnostic model will be. We also used another two datasets E-MTAB-694 (18 disease samples and 17 normal samples) and GSE7307 (18 disease samples and 23 normal samples) to verify the accuracy of the constructed neural network model. In the E-MTAB-694 dataset ( Figure 5C), the AUC values of the seven DEGs were 0.8226, 0.6623, 0.6836, 0.6625, 0.8367, 0.8471, and 0.8617. In the verification results of the GSE7307 dataset ( Figure 5D), the AUC values of the seven DEGs were 0.7464, 0.6484, 0.7020, 0.6300, 0.9075, 0.8295, and 0.8327. In general, we constructed a novel diagnostic model of EMs and verified its diagnostic efficacy through the constructed artificial neural network in two public datasets.

DISCUSSION
The combination of random forest and artificial neural network can be used to construct a reliable predictive model for the diagnosis of some diseases, such as polycystic ovary syndrome (PCOS) (Xie et al., 2020) and ulcerative colitis . In this study, we identified 2,552 DEGs associated with EMs in the GSE51981 dataset. Based on the random forest classifier, seven important candidate DEGs (COMT, NAA16, CCDC22, EIF3E, AHI1, DMXL2, and CISD3) were screened. Then, we used the GSE6364 dataset as the training set to construct the artificial neural network model and evaluated the classification efficacy of the model in E-MTAB-694 and GSE7307 datasets. The AUC values of the ROC curve were about 0.7, which had great efficiency and verified the diagnostic efficacy of the model. Furthermore, we constructed a 15-hub-gene-based PPI network and confirmed the reliability of the prediction model. Compared with the Nnet package, we found that the neuralnet package had higher accuracy of the predicted model (86.5% vs 81.1%). In total, the constructed diagnostic model could provide new insight into our understanding of the pathogenesis of EMs and identify Among these seven genes, COMT, NAA16, CCDC22, and EIF3E have been reported to be associated with the pathogenesis of EMs. Catechol-O-methyltransferase (COMT) is highly expressed in the placental, adrenal gland, ovary, and other tissues. The degradative pathways of the catecholamine transmitters can relieve painful uterine contractions (D'Astous- Gauthier et al., 2021). COMT polymorphism may contribute to the risk of EMs and adenomyosis  and has a relationship with EM susceptibility (Ji et al., 2017;Zhai et al., 2019). N-alphaacetyltransferase 16 (NAA16) is highly enriched in bone marrow, testis, endometrium, and other tissues. It can alter NAT 2 enzyme activity and thus contribute to the susceptibility of EMs (Nakago et al., 2001). Coiled-coil domain containing 22 (CCDC22), a membrane-binding protein, is highly enriched in the spleen, lymph node, and other tissues. Studies have demonstrated that there is also a relationship between CCDC22 polymorphisms and EM susceptibility (de Oliveira Francisco et al., 2017). Eukaryotic translation initiation factor 3 subunit E (EIF3E) is highly expressed in the ovary, lymph node, endometrium, and other tissues. Its downregulation may be involved in epithelial-mesenchymal transition (EMT) in EMs, possibly through the preferential translation of snail (an inhibitor of E-cadherin) (Cai et al., 2018) and involved in the development of adenomyosis through activating the TGF-β1 signaling pathway (Cai et al., 2019).
Interestingly, we identified another three important genes (AHI1, DMXL2, and CISD3), which have never been reported to be involved in the pathogenesis of EMs. Abelson helper integration site 1 (AHI1) is highly enriched in testis, adrenal gland, brain, prostate, endometrium, and other tissues, which has upregulated expression level in EMs. The AHI1 protein participates in reactive oxygen species (ROS) production in the form of protein complexes (Liu et al., 2017). Excessive production of ROS can result in oxidative stress (OS) and overall immune activation and inflammation (Newsholme et al., 2016). OS represents an imbalance between ROS and antioxidants, which may have an essential role in the endometriosis pathogenesis in the peritoneal cavity (Samimi et al., 2019). Hence, the AHI1 protein may participate in the EMs pathogenesis through multiple processes such as OS and immune and inflammatory response. Dmx like 2 (DMXL2) encodes a protein with 12 WD domains, which has relatively low expression in endometrium tissue and downregulated expression in EMs. The DMXL2 protein is demonstrated to participate in the regulation of the Notch signaling pathway (Sethi et al., 2010) and acts as a transmembrane protein, which can promote EMT through hyperactivation of the Notch signaling pathway (Faronato et al., 2015). Interestingly, decreased Notch signaling can contribute to impaired decidualization through the downregulation of FOXO1 (a downstream target of Notch signaling) and thus lead to the pathogenesis of EMs (Su et al., 2015). Furthermore, studies indicate that a circRNA with downregulated expression can regulate EMT in EMs via the Notch signaling pathway (Zhang et al., 2019). Therefore, the downregulated expression of DMXL2 may activate the Notch signaling pathway, contribute to EMT through the interaction with circRNA, and thus lead to the pathogenesis of EMs.
CDGSH iron sulfur domain 3 (CISD3) is a member of the CDGSH domain-containing family, whose expression is upregulated in EMs. The CISD3 protein is redox active and is thought to play an important role in mitochondrial homeostasis (Geldenhuys et al., 2019). Studies indicate that mitochondrial homeostasis can be considered as the therapeutic target for the treatment of EMs via limiting ESC migration and promoting apoptosis (Suliman and Piantadosi, 2016;Zhao et al., 2018). Furthermore, excessive mitochondrial fission can initiate caspase 9-related mitochondrial apoptosis and thus lead to cell death (Fuhrmann and Brüne, 2017;Zhou et al., 2017). Therefore, upregulated expression of CISD3 may affect mitochondrial homeostasis and thus play an important role in the pathogenesis of EMs.
In this study, based on random forest and artificial neural network algorithm, we established a novel reliable diagnostic model and screened out three important DEGs that have never been reported to be involved in the pathogenesis of EMs. We aimed at the supplement of existing methods and provided an alternative marker panel for further research in the early screening of EMs. However, there are some limitations for this study. Firstly, all samples are only classified as EM (disease) and non-EM (normal) groups, which may affect the final screening results of DEGs. Secondly, the diagnostic model is only verified in two public datasets, which need more samples for verification. Thirdly, we conduct data analysis only at the mRNA level in the tissue samples of EMs, which require further validation at the mRNA and protein levels. In general, our approach has a certain clinical value, which can be beneficial for the early screening of EMs.

DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.

AUTHOR CONTRIBUTIONS
JS performed data preprocessing and data analysis and wrote the first draft. DS gave advice on the data analysis. RD and LW gave constructive advice for the whole project.


s2fieldsofstudy: Biology Medicine

text:
The establishment of a cytomegalovirus -specific CD8+ T-cell threshold by kinetic modeling for the prediction of post-hemopoietic stem cell transplant reactivation

Summary The dynamic interaction between the CMV virus and host immune response remains obscure, thus hindering the diagnosis and therapeutic management of patients with HSCT. The current diagnosis of CMV viremia depends on viral load estimation. Medical intervention based on viral load, can be unnecessary or poorly timed for many patients. Here we examined the clinical features and blood samples of patients with HSCT and assessed the CMV reactivation kinetics and corresponding CMV antigen-specific T-cell response in individual patients based on a peptide pool stimulation T-cell assay, which showed that CMV-specific CD8+ T cells were more suitable to be a diagnosis indicator for suppressing CMV reactivation. Using ROC analysis, we defined and verified a CMV-specific CD8+ T-cell counts threshold (925 cells/106 PBMCs) as an indicator of CMV reactivation post-HSCT, and suggested that use of this threshold would provide more accurate guidance for prompt medication and better management of CMV infection post-HSCT.

INTRODUCTION
Cytomegalovirus (CMV) infection is one of the major causes of morbidity and mortality following allogeneic hemopoietic stem cell transplant (HSCT), which is used to treat a variety of blood disorders, including non-malignant diseases, e.g., aplastic anemia, and hematologic malignancy, e.g., leukemia, lymphoma and multiple myeloma (de Koning et al., 2016;Simons et al., 2019). For a period of time after HSCT, recipients' immunity remains depleted and suppressed while the reconstitution of donorderived host immunity is in progress, leaving HSCT recipients vulnerable to opportunistic infections. Indeed, at this stage, pre-existing latent CMV from the host or graft can flare up within the recipient (Liu et al., 2015;Moss and Rickinson, 2005). It is known that CMV reactivation occurs in approximately 60% to 70% of seropositive patients with HSCT within the first 100 days post-HSCT, and without prompt additional viral suppression treatment up to 10% of patients progress into potentially fatal CMV diseases which include pneumonitis, gastritis, enteritis, colitis, hepatitis, retinitis, and encephalitis (Boeckh et al., 2003;Einsele et al., 2020;Goldsmith et al., 2016;Hodowanec et al., 2020;Lee et al., 2017;Maertens and Lyon, 2017).
Currently, CMV reactivation is diagnosed through the estimation of blood CMV titer by CMV-DNA and -antigen (pp65) tests (Berth et al., 2016;Kamei et al., 2016). However, these snapshots of blood viral load are far from satisfactory for the clinical assessment of overall disease severity or prediction of disease outcomes. A CMV-DNA positive result does not always manifest any CMV disease, whereas a negative CMV-DNA result cannot rule out the development of CMV diseases. Consequently, in the hospitals of our study cohort, a 3-month prophylactic antiviral therapy with antiviral agents (ganciclovir, valganciclovir or foscarnet) is routinely applied following HSCT to reduce the chance of CMV recurrence (Boeckh and Ljungman, 2009;Sellar and Peggs, 2012). Then, pre-emptive approach after the first 90 days is performed involving close viral load monitoring and antiviral treatment for significant viral reactivation. However, the prolonged use of these antiviral drugs can cause adverse effects on bone marrow and kidneys and may lead to the selection of drug-resistant strains of CMV (Boeckh and Ljungman, 2009;Heagy et al., 1991;Ljungman et al., 2010). Moreover, the high costs of anti-viral therapy impose a huge financial burden on patients whose medical costs are not fully covered by insurance. Thus, from a clinical perspective, there is an urgent need to develop improved diagnostic methods to monitor CMV reactivation and guide targeted antiviral therapy.
Previous studies, in a mouse model and in human HSCT cases, have shown that CD8 + T-cells, CD4 + T-cells, natural killer (NK) cells, and B cells all play important roles in controlling CMV reactivation (Alegre, 2019;Apiwattanakul et al., 2020;Chang et al., 2014;Rogers et al., 2020). However, the reconstitution of recipients' adaptive immunity through the engraftment of donor stem cells is a slow process, which occurs over months to years (van den Brink et al., 2015). In addition, the re-establishment of host immunity can be affected by multiple HSCT-related factors such as recipient and donor CMV serostatus, the stem cell source, the degree of human-leukocyte-antigen (HLA) matching, infections, presence of graft-versus-host disease (GVHD) and degree of corticosteroids use for immune suppression (Pei et al., 2017). The variations of these factors in individual patients make it difficult to accurately evaluate the true extent of immune reconstitution after HSCT. To date, no standardized index or threshold has been established to indicate an effective immunity for controlling CMV reactivation (Camargo et al., 2019;Tey et al., 2013). Furthermore, during the immune reconstitution, fluctuations of recipients' immunity occur frequently, which may allow CMV recurrence. Therefore, a better understanding of the dynamic relationship between host immune responses, particularly T-cell immunity, and CMV reactivation is important for better management of post-HSCT CMV-mediated complications (Wagner-Drouet et al., 2021).
In this study, we analyzed blood samples of 88 patients from Guangzhou First People's Hospital (GFPH) and 43 patients from the First Affiliated Hospital of Guangzhou Medical University (GMUH) who had undergone HSCT. CMV-specific T-cell immunity (CD4 + and CD8 + ) assays were performed based on a peptide pool stimulation T-cell assay and CMV titers were measured (via DNA) at various timepoints over a period of 90 days to 3 years post-HSCT. Reiterative computational data-fitting allowed us to develop a mathematical model through which a significant correlation was established between CMV reactivation kinetics and the antigen-specific CD8 + T-cell response. Importantly, by analysis of patients from two different hospitals, we were able to define a threshold of CMV-specific CD8 + T-cell counts (per million PBMCs) that corresponded, in most cases, to CMV containment. We then verified the use and robustness of the threshold in separate patient cohorts. Thus, we defined a new parameter: CMV-specific CD8 + T-cell counts per million PBMCs, which has the potential to be used as an effective cue for anti-viral therapy.

Clinical characteristics of patients and incidence of cytomegalovirus viremia following hemopoietic stem cell transplant
In this study, two cohorts of patients with HSCT, diagnosed with acquired severe aplastic anemia (SAA) (cohort I: 64 patients) and hematologic malignancy (HM) (cohort II: 24 patients), respectively, were recruited from Guangzhou First People's Hospital (GFPH), the largest SAA diagnosis and treatment center in South China (Table 1). Additionally, 21 patients with HSCT, each diagnosed with various forms of HM, were recruited from a different hospital, the First Affiliated Hospital of Guangzhou Medical University (GMUH) (Cohort III, Table 1). The clinical characteristics of the patients, including donor source, stem cell source, donor/recipient CMV serostatus, and the presence or absence of GVHD, were summarized in Table 1.
To analyze CMV-DNAemia occurrence in the three cohorts, we performed quantitative real-time PCR using plasma collected at various timepoints between 2 weeks before and 3 years after HSCT. The diagnostic criterion of CMV viremia was defined as 500 copies/mL in plasma (clinical threshold for treating). Based on the medical records and laboratory data, the occurrence of viremia over time was assessed. For the patient cohorts from GFPH, the accumulative CMV incidences were 80.3% (74.7% at d60) and 61.4% (47.8% at d60) for HSCT patients with SAA (cohort I) and HM (cohort II), respectively (Figures S1A and S1B). For cohort III (patients with HM from GMUH), the overall cumulative CMV incidence was 61.7% (51.3% at d60) ( Figure S1C), similar to that of cohort II (p = 0.8443). Collectively, these results show that the incidence of CMV reactivation rises rapidly before d60 and reaches a plateau at around d100, in agreement with previous clinical observations (Green et al., 2012(Green et al., , 2013 The large set of PCR data (1775 samples from 88 patients) obtained from the GFPH cohorts (I & II) allowed us to investigate the kinetics of CMV reactivation. We first produced a heat map for both patients with SAA and HM that displays the distribution and level of CMV viremia over a period of 330 days ( Figure 1A). A very low CMV incidence was seen within the first 30 days after HSCT ( Figures 1A and 1B). This was followed by a large increase in the detection of CMV between d30-60. After d60, the incidence of CMV detection declined ( Figures 1A and 1B).
To further analyze the dynamic distribution of CMV viral loads, we selected patients who had one episode of CMV reactivation within 90 days post-HSCT (43 from the 88 patients) ( Figure 1C) and fitted their DNA data into a single ordinary differential equation (for details see STAR methods). After reiterate computational modeling, a representative CMV-DNA kinetic curve was generated ( Figure 1D) from the data of individual cases ( Figure S2). Consistent with our conclusions from the heat map visualization, the bulk of CMV iScience Article reactivation occurred between d29-64 ( Figure 1E). In addition, we also obtained the following important information with respect to post-HSCT CMV reactivation: (i) the median latency period for CMV replication to reach the levels of >500 copies/mL in plasma was 25 days with a minimum of 17 days and a maximum of 35 days (min-max range: 17-35 days); (ii) the median period for CMV to reach the peak level (2403.4 iScience Article copies/mL) was 44 days (min-max range: 29-64 days); and (iii) the median duration of infection resulting from the first episode of CMV reactivation was 51 days (min-max range: 33-63 days) ( Figure 1E).

The dynamics of cytomegalovirus-specific CD8 + T-cell responses after hemopoietic stem cell transplant
To understand the dynamics of post-HSCT T-cell immune reconstitution, based on a peptide pool stimulation T-cell assay, we measured the total CMV-specific (IFNg + ) CD8 + T-cells from the blood samples of the GFPH cohorts (88 patients) collected at 280 time points over 330 days and generated a heat map (Figures 2A and S3). A likelihood ratio test of the experimental data revealed that the levels of CMV-specific CD8 + T-cells produced within a window of 35 days between d50-85 were significantly higher than those observed between d15-50 (p < 0.001, n = 56) but lower than those seen between d85-120 (p = 0.0068, n = 53) ( Figure 2B).
Next, we generated a CMV-specific CD8 + T-cell response curve by fitting a single ordinary differential equation with the data from the 68 (out of 88) patients ( Figures 2C and S4) who had at least one CD8 + T-cell measurement within 180 days post-HSCT (as described in STAR methods). The response curve (Figure 2D) displayed a temporal pattern of three phases (I, II, and III) which corresponded to the three post-HSCT CMV infection phases (phase I: latency, phase II: duration of reactivation, and phase III: resolution). During phase I, the titers of the CMV-specific CD8 + T-cells in most patients remained extremely low (9.1 per million PBMCs) and the median length of this phase was 20 days (range: 10-44 days) ( Figure 2E). Given that the latency period for CMV viremia was 25 days (range: 17-35 days) ( Figure 1E), we inferred that some CMV antigens were expressed and could stimulate T-cell response, which could be detected by our T-cell assay before CMV-DNA reached 500 copies/mL to be defined as CMV viremia, our data suggested the CD8 + T-cell response commenced during or immediately after the start of CMV reactivation.
In phase II, the CMV-specific CD8 + T-cells increased rapidly, eventually reaching the median level of 1055 cells/million PBMCs prior to the viral resolution phase ( Figures 2D and 2E). During phase III, the levels of the CMV-specific CD8 + T-cells began to plateau with a median of 1976.6 CMV-specific CD8 + T-cells/million PBMCs ( Figures 2D and 2E) around d180.
We also examined the dynamic changes of CMV-specific CD8 + T cells for 25 patients with no detectable CMV viremia in the blood (other patients were diagnosed with single CMV viremia (n = 50) or repeated CMV viremia (n = 13)). Data showed that over time there was a significant increase in the levels of CMV-specific CD8 + T-cells in these 25 patients ( Figures S5A and S5B). It's probably owing to the fact that the viral load in plasma didn't meet the diagnostic (<500 copies/mL), or CMV reactivation might have occurred in tissues other than blood. Furthermore, we couldn't exclude the possibility that CMV-specific CD8 + T-cells were recovered by other immune mechanisms thus preventing CMV reactivation.

The dynamics of cytomegalovirus-specific CD4 + T-cell responses after hemopoietic stem cell transplant
To analyze the dynamics of CMV-specific CD4 + T-cell responses post-HSCT, we used the same blood samples of the GFPH cohorts (I and II) for the analysis of the CD8 + T-cell responses to generate a CD4 + T-cell response heatmap ( Figure 3A) and perform a likelihood ratio test of CMV-specific CD4 + T-cell levels over three different periods ( Figure 3B). Unlike what was observed for CD8 + T-cells, there was no significant difference in the CD4 + levels between (d15-50], (d50-85] and (d85-d120] ( Figure 3B). As in our above analysis of CD8 + T-cells, 68 (out of 88) patients, who had at least one CD4 + T-cell measurement within 180 days post-HSCT, were selected in the fitting of the CD4 + T-cell model ( Figure 3C). The frequency of responding CD4 + T-cells producing IFN-g was around 396.3 (309.2-422.6) cells/million PBMCs upon viral resolution, which was much lower than for CD8 + T-cells. At d180, the mean level of virus-specific CD4 + slightly decreased to 158.6 (2.54-788.5) cells/million PBMCs ( Figures 3D and 3E), approximately 1/10 of those of the CD8 + T-cells ( Figures 2E and 3E), showing that although CMV reactivation was able to elicit a CD4 + T-cell response , it increased during CMV viremia then declined upon suppression of the virus. The individual patient data fitting of the CD4 + T-cell model was shown in Figure S6. The lower magnitude and decline after the viral resolution of CMV-specific CD4 + T-cells, compared to CD8 + T-cells, were further exemplified by four representative patients with multiple time points across 180 days where CD8 + or CD4 + T-cell counts and data fitting curves were compared ( Figures S7A-S7C).

OPEN ACCESS
iScience 25, 105340, November 18, 2022 5 iScience Article Defining an antigen-specific-CD8 + T-cell threshold as an indicator for cytomegalovirus containment The demonstration of a strong correlation of the antigen-specific-CD8 + T-cell counts with the suppression of CMV replication and a close connection of the kinetic profiles of CMV distribution and CD8 + response ( Figure 2D) suggested that antigen-specific CD8 + T-cells could be used as an indicator for CMV containment. Hence, we next sought to define a CMV-specific CD8 + T-cell resolution threshold for the prediction of CMV reactivation.  Tables S2-S5), with corresponding CMV-DNA and CMV-specific CD8 + T-cell data, were used in a receiver operating characteristic curve (ROC) analysis to evaluate the performance of the immunological detection of CMV viremia (Figure 4). We plotted the true positive rate (sensitivity) against false-positive rate (represented as: 1-specificity). The area under the curve (AUC) calculated using the trapezoid rule (see STAR methods) was 0.727 ( Figure 4A), indicating an effective measure of accuracy in using immunological detection as a diagnostic test (Hajian-Tilaki, 2013). Since this study aims to achieve a safe threshold to guide the start and end of anti-viral treatment, sensitivity is more important than specificity, therefore, we chose to concentrate on a part of the ROC curve in which the sensitivity rose sharply (0.74-0.88) without much change in specificity (0.52-0.54) ( Figure 4A, indicated in the  Table S1). At this threshold, 62.8% had expected diagnostic outcomes, which included 96 T + V-timpoints (CD8 + T-cells (T value) above 925 cells/10 6 PBMCs and CMV loads (V value) below 500 copies/mL) and 59 T-V+ timepoints (CD8 + T-cells below 925 cells/10 6 PBMCs and CMV-DNA levels above 500 copies/mL) ( Figure 4C and Tables S1-S3). In contrast, 12 of 247 timepoints showed CMV virus titers higher than 500 copies/mL with corresponding CMV-specific CD8 + T-cell counts above the T-threshold (T + V+) and 80 timepoints showed no CMV viremia detected while CMV + CD8 + T-cells were lower than the T-threshold (T-V-) ( Figure 4C and Tables S1, S4, and S5).
Based on the dynamic computational model ( Figure 2D), the time to reach the threshold (925 specific CD8 + T-cells/million PBMCs) was predicted to be 69 days (range: 35-112 days). Given that the presence of patients with CMV viremia following HSCT persists between d17 (the start of latency) and d98 (the sum of the longest latency period, 35 days, and the maximum duration of infection, 63 days, Figure 1E), the clearance of viral replication correlated well to the magnitude of CMV-specific CD8 + T-cell response. Furthermore, our model also indicated that the median level of CMV-specific CD8 + T-cell counts at the initial point of the viral resolution phase was 1055 (range: 12-2913 cells/million PBMCs) ( Figures 2D and 2E).

Threshold were verified in the clinical context and showed great power in predicting the status of cytomegalovirus reactivation
To evaluate the power of T-threshold in predicting CMV reactivation clearance, we studied the kinetics of CMV-DNA and CMV-specific CD8 + T-cell from multiple timepoints ( Figure 5 and Tables S2-S5). Interestingly, based on a total of 155 timepoints with T+V-and T-V+, we found the CD8 + T-cells were maintained above the T-threshold, at the same time, the CMV replication was effectively suppressed, in contrast, when CD8 + T were lower than threshold, CMV-DNA were detected ( Figure 5A and Tables S2 and S3). It showed that the defined T-threshold value reflects the switch point between CMV reactivation and CMV-specific CD8 + T immunity within individual patients with HSCT ( Figure 5D).
Case analyses of the 12T+V+ timepoints which comprised 4.9% of patients showed that, without the complication of a relapse of pre-HSCT disease, when patients' T-cell counts climbed above the T-threshold of 925 cells/10 6 PBMCs, their blood CMV would be cleared with a short delay of 2-9 days (Figure 5B and Table S4). Thus, the T-threshold could predict subsequent viral clearance and could be used as a safe indication index for stopping anti-viral therapy ( Figure 5E).  (Table S5). Of them, 20 timepoints from 6 patients were found to fail to generate adequate levels of CMV-specific CD8 + T-cells over a prolonged period after HSCT and suffered multiple episodes of CMV viremia ( Figure 5C, blue dot in green cycle). Consequently, four of them developed CMV retinitis, above facts showed only after the establishment of adequate levels of T-cell immunity would be able to avoid further CMV infection, emphasizing the importance of using the immunological test combining CMV-DNA test in monitoring the development of post-HSCT CMV reactivation. Thus we suggested that patients with no detectable CMV viremia in the blood but with below-threshold counts of CMV-specific CD8 + T-cells still require antiviral treatment ( Figures 5F and 6B). For another 57 timepoints in 29 patients, T-cell immunity levels were still recovering or fluctuating below the threshold but CMV-DNA was not detected, and it occurred mostly within the first 100 days following HSCT ( Figure 5C, blue dot in orange cycle). Under such circumstances, as almost all of these T-V-timepoints were seen within the period of compulsory prophylactic antiviral therapy, they would not affect post-HSCT CMV management ( Figures 5F  and 6B).
In sum, combining the CMV-DNA test, our threshold showed strong power in predicting the status of CMV reactivation and guiding for when to start and stop the anti-viral therapy.

DISCUSSION
Cellular immunity has long been known to play an important role in the control of CMV infection (Klenerman and Oxenius, 2016). However, to date, the dynamic interplay between CMV infection and host T-cell immunity is poorly understood. In this study, we examined the clinical characteristics of patients with HSCT from two separate medical settings and analyzed the kinetic interactions between CMV reactivation and the CMV-specific T-cell response. Owing to the very high CMV-infection rate in the Chinese population, a universal 90-day prophylactic therapy with antiviral drugs is given to all patients with HSCT. Under these conditions, the CMV viremia curve generated through analysis of blood samples of patients at various timepoints displays a typical distribution with the majority of CMV incidence occurring between d17 and d98 after HSCT and the peak of Although lower than the CD8 + T-cell response, the synchrony of the kinetic curves of CD4 + T-cells and CMV-DNA suggests that CD4 + T-cells may also contribute to the inhibition of CMV replication. However, in contrast to CD8 + T-cells, which remained high after 180 days, CD4 + T-cell counts in a significant number of patients declined after their CMV viremia was suppressed. The different patterns of CD4 + and CD8 + T-cell responses may reflect the separate pathways used for the surveillance of CMV and presentation of viral antigens. It is known that CD4 + T-cells predominantly recognize CMV structural proteins whereas CD8 + T-cells can recognize non-structural proteins encoded by immediate-early gene loci, inhibiting CMV transcriptional reactivation before the formation of viral particles (Simon et al., 2006). In addition, the differential responses of CD8 + and CD4 + T-cells to post-HSCT CMV reactivation are in agreement with the view that CMV-specific CD8 + T cells are more suitable to be a diagnosis indicator for suppressing CMV reactivation whereas CD4 + T-cells are important for the maintenance of antiviral immunity including expansion and persistence of CD8 + T-cells (Boeckh and Ljungman, 2009;Meesing and Razonable, 2018).
The ultimate application of using CMV-specific CD8 + T-cell counts to the clinical prognostication of post-HSCT CMV reactivation relies on the standardization of quantification and methodologies for the evaluation of this biomarker. To this end, we used the flow cytometry intracellular cytokine staining (ICS) method to process 247 HSCT samples, then defined the CD8 + T-cell counts, R925 cells/10 6 PBMCs, as the resolution threshold for CMV reactivation using ROC analysis. Recently, Messing and Razonable have reported the correlation of peripheral blood absolute lymphocyte count (PBALC) with CMV infection and proposed the PBALC value, %830/mL, as a clinically relevant threshold for the prediction of CMV infection. In addition, two other studies have demonstrated the utility of the simple and high throughput QuantiFERON-CMV assay to monitor CMV-specific CD8 + T-cells for the prediction of CMV infection in patients with HSCT (Tey et al., 2013;Yong et al., 2017). However, no cut-off points for CD8 + T-cell quantification were defined in these studies. Moreover, in a previous study, Angeles Clari et al. compared the performance of QuantiFERON-CMV assay with that of the ICS method and found the latter to be more sensitive in the detection of CMV-specific CD8 + T-cells for patients with HSCT (Angeles Clari et al., 2012). Our case analyses showed that CMV-specific CD8 + T-cell counts were particularly useful for assessing secondary and recurrence CMV reactivation after the prophylactic period (the first 90 days post-HSCT) and that a stable reconstitution of CMV-specific T-cell immunity as measured by the continuous above-threshold levels of CD8 + T-cells ensured the suppression and clearance of CMV within patients with HSCT. Even with occasional rises in viral replication, the CMV titers would decline to an undetectable level in around 7 days without the administration of antiviral drugs. On the other hand, the immune reconstitution of patients with HSCT can easily be hampered by anti-GVHD treatments or relapse of leukemia, resulting in CD8 + T-cell levels below the threshold for a prolonged period. Under these circumstances, a further pre-emptive antiviral therapy is recommended to prevent CMV-associated diseases.
Patients developing CMV disease correlating with low CD8 + T-cell counts, but without detectable CMV-DNA in plasma, suggesting that CMV reactivation might have occurred in distal tissues/organs and have not spilled over to blood with detectable quantity, supporting our hypothesis that the inability for the CD8 + T-cells to reach the resolution threshold of 925 cells/10 6 PBMCs serves as a warning sign for CMV reactivation even in the absence of detectable levels of CMV in the plasma.
In fact, if we use CD8 + T cells measured after the time of prophylaxis (90 days post-HSCT) to predict viremia that comes at the same time, the sensitivity and specificity of the virological test will be 44.7% and 91.2%, which means that the rate of missed diagnosis (mistakenly to recommend withdrawal) and misdiagnosis (over-medication) of CMV-DNA test is 55.3% and 8.8% (Table S6). Traditionally, after prophylaxis treatment within 90 days post-HSCT, only the CMV-DNA test is used to monitor CMV reactivation, if CMV-DNA positive (V+), antiviral treatment continues, otherwise it stops ( Figure 6A). For future CMV antiviral therapy after 90 days prophylaxis treatment, combining T-cell assay and CMV-DNA test, we want to address two exceptional circumstances ( Figure 6B): 1) when CMV-DNA levels above 500 copies/mL (V+) and T-cell counts higher than T-threshold (T+) are detected after 90 days post-HSCT, the antiviral treatment is advised to stop. 2) when V-and T-are detected after 90 days post-HSCT, we strongly suggested that the antiviral therapy must be implemented although traditionally V-is considering not risky.
HSCT recipients are given prophylaxis or are closely preemptively monitored for the first 100 days after transplant in many countries (Einsele et al., 2020). Traditionally, the pre-emptive approach involves close viral load monitoring and treatment for significant viral reactivation ( Figure 6A). The problem that this type of data could be applied to solving is that it becomes difficult to continue monitoring patients weekly for CMV viral load blood draws indefinitely after transplant. Thus, having a T-cell marker measured around day 90 or 100 that could predict later viremia would be quite helpful. We could use such a marker to avoid having to measure additional viral loads in patients with protective responses after the critical reactivation period.

Conclusions
The dynamic interaction between CMV virus and host immune response remains obscure, thus hindering the diagnosis and therapeutic management of patients with HSCT. In this work, we assessed the CMV reactivation kinetics and corresponding CMV antigen-specific T-cell response in individual patients, which showed that compared to CMV-specific CD4 + T-cells, CMV-specific CD8 + T-cells are more suitable to be a diagnostic indicator for suppressing CMV reactivation. Then, our case analyses indicated that CMVspecific CD8 + T-cell counts are particularly useful for assessing CMV recurrence after the prophylactic period (the first 90 days post-HSCT) and that a stable reconstitution of CMV-specific T-cell immunity as measured by the continuous above-threshold levels of CD8 + T-cells ensured the suppression and clearance of CMV within patients with HSCT. Based on ROC analysis, we defined a CMV-specific CD8 + T-cell threshold (925 cells/10 6 PBMCs) for the immunological detection of CMV viremia. We addressed the utility of the T-cell threshold in two exceptional circumstances: 1) when CMV-DNA levels above 500 copies/mL (V+) and T-cell counts higher than T-threshold (T+) were detected after 90 days post-HSCT, the antiviral treatment was advised to stop. 2) when V-and T-were detected after 90 days post-HSCT, we strongly suggested that the antiviral therapy must be implemented although traditionally V-is considering not risky.
Combining CMV-DNA test, our data suggest that the use of this threshold would provide more accurate guidance for prompt medication and better management of CMV infection post-HSCT. iScience Article CMV reactivation, there are limitations in this study. Owing to around 95% high prevalence of CMV infection in China, we could not validate the threshold those D-/R+ (high-risk) or even D+/R-(low-risk) patients. As a retrospective study, inconsistent timepoints over a large period of time were collected, which had to use complicated fitting mathematical model for each patient for clear interpretation, and our modeling and kinetics also could not tell the causal relationship between CD8 + T-cells and CMV reactivation. Most importantly, we need to point out our T-cell assay should combine CMV-DNA test to guide only after 90 days post-HSCT, instead of a surrogate assay for the CMV-DNA test. Furthermore, this is a bi-centre investigation from one geological area. Scale-up studies with more participants from multiple clinical settings and different geological locations will improve the statistical significance of this analysis and strengthen the conclusion from our investigation.

STAR+METHODS
Detailed methods are provided in the online version of this paper and include the following:


s2fieldsofstudy: Biology Chemistry

text:
Zinc Deficiency Induces Autophagy in HT-22 Mouse Hippocampal Neuronal Cell Line

Zinc is a trace metal vital for various functions in nerve cells, although the effect of zinc deficiency on neuronal autophagy remains unclear. This study aimed to elucidate whether zinc deficiency induced by treatment with N, N, N′, N′-tetrakis (2-pyridylmethyl) ethylenediamine (TPEN), a zinc chelator, affects and alters autophagy activity. In cell viability assays, TPEN showed cytotoxicity in HT-22 cells. TPEN treatment also increased LC3-II levels and the ratio of LC3-II to LC3-I. Western blot analysis showed that phospho-AMP-activated protein kinase levels and the ratio of phospho-AMP-activated protein kinase to total AMP-activated protein kinase increased. Protein levels of the mammalian target of rapamycin and sirtuin 1 decreased following TPEN treatment. When TPEN-treated HT-22 cells were cotreated with autophagy inhibitors, 3-methyladenine (1 mM), or bafilomycin A1 (3 nM), the TPEN-induced decrease in cell viability was exacerbated. Cotreatment with chloroquine (10 μM) partially restored cell viability. The study showed that zinc deficiency induces autophagy and may be cytoprotective in neurons. We expect our results to add a new perspective to our understanding of the neuronal pathology related to zinc deficiency.

Introduction
Zinc is the second most abundant trace metal after iron and plays an essential role in various cellular functions, including transcription, cell signaling pathways, and enzyme activity [1]. The brain has an exceptionally high zinc concentration. Most of the zinc in the brain is protein-bound, and zinc is highly localized in the mossy fiber terminals of the dentate gyrus [2]. Approximately 20% of the global population suffers from dietary zinc deficiency [3]. Several studies have demonstrated that zinc deficiency is associated with cognitive decline [4]. Studies using animal models have also reported that zinc deficiency impairs learning and memory [5][6][7].
Autophagy is a conserved process and is a well-known mechanism by which cells adapt to stress by degrading intracellular components. Given that autophagy plays a vital role in cellular homeostasis, it seems reasonable that autophagy plays an important role when zinc levels are below physiological levels. Approximately 40 autophagy-related genes (ATG) are involved in autophagy and complex regulatory mechanisms, including AMP-activated protein kinase (AMPK) and mammalian target of rapamycin (mTOR), alter their activity. AMPK significantly affects neuronal homeostasis. AMPK senses low intracellular ATP levels and adapts to cellular metabolism and autophagy [8]. AMPK and mTOR regulate autophagy initiation [9]. mTOR, a serine/threonine kinase, is a master regulator of cellular metabolism and the most widely studied regulator of autophagy. Among the two mTOR-including signaling complexes, mTORC1 regulates cell growth, proliferation, and autophagy induction [10]. A recent study has shown that zinc depletion induces autophagy by inhibiting TORC1 in yeast [11].
Recently, many researchers have conducted studies on the effect of zinc on the autophagy process; however, the results have been inconsistent. Some studies have reported Int. J. Mol. Sci. 2022, 23, 8811 2 of 9 that high zinc levels and its ionophores stimulate autophagy [12,13]. However, reports on the effects of zinc deficiency on autophagy are complex. Some studies using the zinc chelator N, N, N , N -tetrakis (2-pyridylmethyl) ethylenediamine (TPEN) after various autophagy-inducing conditions, such as ischemia, have shown that zinc deficiency restores previously induced autophagy [14,15]. However, studies on zinc deficiency models without prior stress exposure to autophagy-related conditions are still lacking. We identified only two studies in yeast, which suggested that zinc deficiency induces autophagy [11,16]. The effect of zinc deficiency on autophagy has not been well studied. Zinc deficiency may cause autophagy in mammalian cells, depending on the cell type [17]. Since the hippocampus contains a large amount of zinc, it is crucial to understand how zinc deficiency affects hippocampal neurons. However, no studies have investigated how zinc deficiency affects hippocampal neuronal autophagy and the important autophagy regulators, mTOR, and AMPK signaling. Therefore, the present study aimed to investigate the effect of zinc deficiency on autophagy in hippocampal neurons and its action mechanism.  Figure 1A). To further clarify the reason for the decrease in cell viability, we measured the cytotoxicity of TPEN using an LDH assay. In the assay result, cytotoxicity significantly increased when treated with 1 µM TPEN for 72 h (p < 0.05, Figure 1B). Furthermore, zinc sulfate cotreatment restored the TPEN-induced decrease in cell viability (p < 0.01; Figure 1C). the effects of zinc deficiency on autophagy are complex. Some studies using the zinc chelator N, N, N′, N′-tetrakis (2-pyridylmethyl) ethylenediamine (TPEN) after various autophagy-inducing conditions, such as ischemia, have shown that zinc deficiency restores previously induced autophagy [14,15]. However, studies on zinc deficiency models without prior stress exposure to autophagy-related conditions are still lacking. We identified only two studies in yeast, which suggested that zinc deficiency induces autophagy [11,16]. The effect of zinc deficiency on autophagy has not been well studied. Zinc deficiency may cause autophagy in mammalian cells, depending on the cell type [17]. Since the hippocampus contains a large amount of zinc, it is crucial to understand how zinc deficiency affects hippocampal neurons. However, no studies have investigated how zinc deficiency affects hippocampal neuronal autophagy and the important autophagy regulators, mTOR, and AMPK signaling. Therefore, the present study aimed to investigate the effect of zinc deficiency on autophagy in hippocampal neurons and its action mechanism.  Figure 1A). To further clarify the reason for the decrease in cell viability, we measured the cytotoxicity of TPEN using an LDH assay. In the assay result, cytotoxicity significantly increased when treated with 1 μM TPEN for 72 h (p < 0.05, Figure 1B). Furthermore, zinc sulfate cotreatment restored the TPEN-induced decrease in cell viability (p < 0.01; Figure 1C).

Zinc Deficiency by TPEN Treatment Changed Autophagy-Related Signaling
To determine the mechanism by which TPEN treatment affects autophagy-related signaling, we treated TPEN for 24 h and measured AMPK and phospho-AMPK levels using western blotting. We also performed real-time PCR and western blotting analyses on TPEN-treated HT-22 cells (24 h) and measured SIRT1 and mTOR mRNA and protein levels, respectively.

Zinc Deficiency by TPEN Treatment Changed Autophagy-Related Signaling
To determine the mechanism by which TPEN treatment affects autophagy-related signaling, we treated TPEN for 24 h and measured AMPK and phospho-AMPK levels using western blotting. We also performed real-time PCR and western blotting analyses on TPEN-treated HT-22 cells (24 h) and measured SIRT1 and mTOR mRNA and protein levels, respectively.
Treatment with the autophagy inhibitor Baf A1 at a concentration of 3 nM did not significantly affect the cell viability ( Figure 3B). When we added 3 nM Baf A1 to 1 μM TPEN-treated cells, the decrease in cell viability by TPEN worsened in the group treated with Baf A1 and TPEN (TPEN group: 46.4 ± 7.1% of the control group, TPEN plus BafA1 group: 26.4 ± 3.1% of control group, p < 0.001) ( Figure 3B).

Discussion
In this study, we demonstrated that zinc deficiency induces autophagy and changes AMPK and mTOR signaling in HT-22 cells, a hippocampal neuronal cell line.
Autophagy is generally considered to have a cytoprotective effect because when a cell is stressed, it immediately supplies the necessary nutrients by breaking down the intracellular components. Autophagy is an intracellular process that maintains neuronal viability and plays a pivotal role in neuronal homeostasis in neurodegenerative diseases by degrading misfolded proteins and damaged organelles [18]. However, cytotoxicity and cell death may occur when the autophagy activity exceeds a certain threshold [19]. The Treatment with the autophagy inhibitor Baf A1 at a concentration of 3 nM did not significantly affect the cell viability ( Figure 3B). When we added 3 nM Baf A1 to 1 µM TPEN-treated cells, the decrease in cell viability by TPEN worsened in the group treated with Baf A1 and TPEN (TPEN group: 46.4 ± 7.1% of the control group, TPEN plus BafA1 group: 26.4 ± 3.1% of control group, p < 0.001) ( Figure 3B).

Discussion
In this study, we demonstrated that zinc deficiency induces autophagy and changes AMPK and mTOR signaling in HT-22 cells, a hippocampal neuronal cell line.
Autophagy is generally considered to have a cytoprotective effect because when a cell is stressed, it immediately supplies the necessary nutrients by breaking down the intracellular components. Autophagy is an intracellular process that maintains neuronal viability and plays a pivotal role in neuronal homeostasis in neurodegenerative diseases by degrading misfolded proteins and damaged organelles [18]. However, cytotoxicity and cell death may occur when the autophagy activity exceeds a certain threshold [19]. The precise mechanisms underlying excessive autophagy remain unclear. The mechanism by which autophagy protects hippocampal neurons from zinc deficiency has not yet been elucidated. A study in yeast showed that zinc deficiency caused autophagy, and genetic blockade of autophagy inhibited cellular growth [11,16]. Metallothioneins (MTs), proteins that bind zinc and buffer intracellular zinc levels, are degraded in lysosomes [20,21]. Other studies have confirmed that lysosomal zinc is transported into the cytosol by the zinc transporters Zrt-and Irt-like protein 8 (ZIP8) and transient receptor potential cation channel mucolipin subfamily member 1 (TRPML1) [22,23]. Zinc recycling occurs through MTs degradation and zinc transport by autophagy, and intracellular zinc levels can be restored. Notably, intracellular free zinc levels reportedly decrease when drugs or genetic methods are used to inhibit autophagy [15,24].
After treatment with 3-MA, or bafilomycin A1, and autophagy inhibitors, together with TPEN, our experimental results suggested that autophagy has a cytoprotective effect in a zinc deficiency state. Although chloroquine is known to inhibit lysosomal function and act as an autophagy inhibitor, it reportedly enhances zinc uptake [25]. In this study, we also observed that treatment with 10 M chloroquine induced cell proliferation and attenuated the decrease in cell viability following TPEN treatment.
AMPK signaling is activated by sensing the level of available energy in the cell. Activated AMPK increases catabolism and decreases anabolism. In this study, zinc deficiency induced by TPEN treatment activated AMPK via AMPK Thr 172 phosphorylation. The AMPK activation mechanisms include changes in the ATP-to-AMP ratio and upstream kinases, such as tumor suppressor liver kinase B1 (LKB1) and calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) [8]. Mechanisms related to AMPK activation causing autophagy have been studied, including unc-51-like kinase 1 (ULK1) activation by its phosphorylation, beclin1 phosphorylation, and p53 attenuation [9,[26][27][28]. Metformin, an antihyperglycemic agent which activates neuronal AMPK at Thr172, reportedly restored the decrease of hippocampal synaptic density in the sevoflurane-induced memory impairment animal model by activating autophagy [29]. Furthermore, TPEN treatment decreased protein levels of mTOR and SIRT1. As the mRNA expression of both genes increased, the decrease in protein amount may be due to an increase in degradation. There are few studies on changes in the amount of mTOR protein associated with autophagy. One study showed that the mechanism of autophagy activation by a halofuginone-induced amino acid response in cancer cell lines is the proteasomal degradation of mTOR [30]. A decrease in mTOR protein levels due to zinc deficiency may also occur through this mechanism. SIRT1 is a well-known autophagy regulator. Caloric restriction increases SIRT1 expression [31], which in turn positively regulates autophagy [32][33][34]. One study showed that reactive oxygen species reduce the amount of SIRT1 protein [35], and another study showed that in the aging process, SIRT1 is a substrate of the autophagic process [36]. The reduction of SIRT1 protein levels by TPEN may be an acute negative feedback mechanism to prevent excessive autophagy. Follow-up studies will elucidate the significance of the zinc deficiency-induced decrease in SIRT1 and mTOR protein levels on neuronal function.
There are limitations to this study. TPEN does not selectively chelate zinc but affects cellular iron and copper levels. However, the experimental results showed that zinc sulfate treatment completely restored the TPEN-induced decrease of cell viability, and treatment with chloroquine, a zinc ionophore, also significantly restored cell viability [37]. Therefore, we can suggest that zinc deficiency may account for a large part of the effect of TPEN on cell viability. In addition, when we used autophagy inhibitors to investigate the role of TPEN-induced autophagy, autophagy inhibitors reduced cell viability at a particular concentration in the HT-22 cell line (data not shown). Therefore, we treated inhibitors at concentrations that did not significantly affect cell viability (bafA1) or had a statistically significant but modest effect (3-MA).
In conclusion, this study revealed that zinc deficiency induces autophagy and alters autophagy-related signaling in hippocampal neurons. In addition, we demonstrated that zinc deficiency-induced autophagy had a cytoprotective function in neurons.

Lactate Dehydrogenase (LDH) Assay
The cytotoxicity of TPEN in HT-22 cells was measured using an LDH detection kit (#MK401, Takara, Kusatsu, Japan). Cells were seeded on a 96-well plate (1 × 10 5 cells/100 µL) and treated with control, 0.1, 0.3, and 1 µM TPEN after 24 h. After 72 h of TPEN treatment, new wells were treated with 1% Triton X-100 (LPS solution, Daejeon, Korea) for 10 min as a positive control, and the medium was centrifuged for 3 min to extract the supernatant. The supernatant was transferred to a new 96-well plate, and the solution contained in the LDH kit was added. The supernatant was incubated with reaction mixtures in the dark for 30 min, and the absorbance was measured with a MultiSkanTM microplate photometer (Thermo Fisher Scientific, Waltham, MA, USA) at 490 nm. We analyzed the LDH assay results using the values of the control wells as negative controls.

Western Blot Analysis
After drug treatment, whole-cell lysates were prepared from HT-22 cells in RIPA buffer (150 mM sodium chloride, 1% Triton X-100, 0.1% SDS, 1% sodium deoxycholate, 50 mM Tris-HCl pH 7.5, 2 mM EDTA, sterile solution) with proteinase and phosphatase inhibitors (ATTO, Tokyo, Japan). The protein samples (20 µg/well) were loaded and separated on 8-15% polyacrylamide gels for 2 h. The proteins (2 h, 100 V) were then transferred onto a polyvinylidene fluoride (PVDF) membrane (Millipore, Burlington, MA, USA). The membranes were blocked with 5% skim milk in Tris-buffered saline pH 7.4 (TBS-T buffer) for 1 h to overnight at 4 • C and then incubated with specific primary antibodies at 4 • C overnight. The following primary antibodies were used: anti-β-actin, anti-mTOR, anti-p-mTOR, anti-SIRT1, anti-LC3, anti-p-AMPK, and anti-AMPK (Cell Signaling Technology, Beverly, MA, USA). We included detailed information on primary antibodies in Supplementary Materials Table S1. After incubation, the membrane was washed thrice with TBS-T and conjugated to mouse or rabbit secondary antibodies (#31430, #31460, Invitrogen, Carlsbad, CA, USA) at room temperature for 1 h. Protein bands were visualized using Forte solution (Millipore, Burlington, MA, USA) or Femto solution (Thermo Fisher Scientific, Waltham, MA, USA) after washing the membranes thrice with TBS-T buffer. The relative densities were quantitatively analyzed using ImageJ 1.53e software (NIH, Bethesda, MD, USA).

Statistical Analysis
All data are presented as mean ± standard deviation (SD). Statistical analyses were performed using SPSS version 20 (IBM, Chicago, IL, USA) for Windows. The statistical significance of differences between groups was evaluated by Student's t-test and one-way analysis of variance (ANOVA) followed by Tukey's multiple range test. p < 0.05 was considered statistically significant.